Multi-Path Siamese Convolution Network for Offline Handwritten Signature Verification

Xian, Zhang; Zhang, Wu; Xie, Liyang; Li, Yong; Li, Fang; Zhang, Jun

doi:10.1145/3512850.3512854

Cited by 9 publications

(3 citation statements)

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“…Figures (7,8,9, and 10) show the graphs of the curves of the results of the proposed study, where the curves are loss, val_loss, val_accuracy, and accuracy with the used dataset. Table 5 shows our results for the (CEDAR) dataset using two methods: the Multi-Path Attention Siamese Convolution Network (MA-SCN) model [32] and A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification [30]. Surroundedness features study using local binary pattern (LBP) and uniform Local Binary Patterns (ULBP) [7].…”

Section: Resultsmentioning

confidence: 99%

“…2) Add a Conv2D layer to the model with a specified number of filters (32), kernel size (3 × 3), and activation function (activation). The input shape was set to the specified input shape (100x100x32).…”

Section: The Feature Extractionmentioning

confidence: 99%

“…4) Add a MaxPooling2D layer was added to the model with a specified pool size (2 × 2). 5) Add another Conv2D layer to the model with a specified number of filters (32), kernel size (5 × 5), padding, and activation function (activation). 6) Add another MaxPooling2D layer was added to the model with a specified pool size (2 × 2).…”

Section: The Feature Extractionmentioning

confidence: 99%

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Developing a Model for Offline Signature Verification Using CNN Architectures and Genetic Algorithm

Musleh,

Al-Azzani

2023

jast

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The signature verification process has many applications, such as its use in financial operations, providing the electronic signature of documents, and providing an additional confidentiality standard to verify the identity of users in computer systems. This process has the advantage of being accepted by the community and is less intrusive compared to other biological methods. Deep learning (DL) and CNN (Convolutional Neural Network) are widely used by bioinformaticians. Due to the difficulty in extracting features in other systems or models, DL and CNN-based signature verification systems have been significantly improved. Yet Hyperparameter optimization for CNN models remains a challenging problem in designing highly efficient models with the most accurate results. It is often convolutional neural network (CNN) models that are manually designed. The proposed method is focused on a genetic algorithm that develops a population of CNN models in order to find the best fit architecture for designing an offline signature verification model. Our model is tested on more than one dataset, BHSig260-Bengali, BHSig260-Hindiin, GPDS, and CEDAR. The result of the approach proposed in this paper has the highest discrimination rate of FRR of 2.5, FAR 3.2, EER 2.35, and 97.73 %-accuracy rate.

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Section: Resultsmentioning

confidence: 99%

Section: The Feature Extractionmentioning

confidence: 99%

Section: The Feature Extractionmentioning

confidence: 99%

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